Overview

Dataset statistics

Number of variables23
Number of observations16786
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory165.0 B

Variable types

Categorical10
Numeric12
DateTime1

Alerts

newsdesk has a high cardinality: 63 distinct values High cardinality
abstract has a high cardinality: 16543 distinct values High cardinality
keywords has a high cardinality: 15412 distinct values High cardinality
uniqueID has a high cardinality: 16786 distinct values High cardinality
lead_paragraph has a high cardinality: 14994 distinct values High cardinality
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
comment_isHigh is highly correlated with newsdesk and 1 other fieldsHigh correlation
newsdesk is highly correlated with comment_isHigh and 2 other fieldsHigh correlation
section is highly correlated with comment_isHigh and 1 other fieldsHigh correlation
material is highly correlated with newsdeskHigh correlation
newsdesk is highly correlated with section and 7 other fieldsHigh correlation
section is highly correlated with newsdesk and 6 other fieldsHigh correlation
material is highly correlated with newsdesk and 5 other fieldsHigh correlation
n_comments is highly correlated with comment_isHighHigh correlation
TEXT_Keywords_POS_NOUN is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_POS_NOUN and 2 other fieldsHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with newsdesk and 2 other fieldsHigh correlation
TEXT_Keywords_ENT_LOC is highly correlated with sectionHigh correlation
TEXT_Keywords_ENT_PERSON is highly correlated with newsdesk and 2 other fieldsHigh correlation
comment_isHigh is highly correlated with newsdesk and 6 other fieldsHigh correlation
newsdesk_num is highly correlated with newsdesk and 5 other fieldsHigh correlation
section_num is highly correlated with newsdesk and 5 other fieldsHigh correlation
material_num is highly correlated with newsdesk and 5 other fieldsHigh correlation
abstract is uniformly distributed Uniform
uniqueID is uniformly distributed Uniform
uniqueID has unique values Unique
word_count has 451 (2.7%) zeros Zeros
TEXT_Keywords_POS_NOUN has 2329 (13.9%) zeros Zeros
TEXT_Keywords_POS_PNOUN has 779 (4.6%) zeros Zeros
TEXT_Keywords_ENT_ORG has 5460 (32.5%) zeros Zeros
TEXT_Keywords_ENT_GPE has 8526 (50.8%) zeros Zeros
TEXT_Keywords_ENT_LOC has 16219 (96.6%) zeros Zeros
TEXT_Keywords_ENT_PERSON has 5541 (33.0%) zeros Zeros

Reproduction

Analysis started2022-01-29 19:37:34.944937
Analysis finished2022-01-29 19:38:15.699208
Duration40.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

newsdesk
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
OpEd
1996 
Foreign
 
1073
Culture
 
1049
Business
 
981
Washington
 
843
Other values (58)
10844 

Length

Max length20
Median length7
Mean length7.054390564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowU.S.
2nd rowOpEd
3rd rowOpEd
4th rowOpEd
5th rowOpEd

Common Values

ValueCountFrequency (%)
OpEd1996
 
11.9%
Foreign1073
 
6.4%
Culture1049
 
6.2%
Business981
 
5.8%
Washington843
 
5.0%
Metro820
 
4.9%
Science750
 
4.5%
National715
 
4.3%
Learning691
 
4.1%
RealEstate682
 
4.1%
Other values (53)7186
42.8%

Length

2022-01-29T13:38:15.818217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oped1996
 
11.8%
foreign1073
 
6.4%
culture1049
 
6.2%
business990
 
5.9%
washington843
 
5.0%
metro820
 
4.9%
science750
 
4.4%
national715
 
4.2%
learning695
 
4.1%
realestate682
 
4.0%
Other values (56)7269
43.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

section
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
U.S.
2364 
Opinion
2272 
World
1183 
Arts
1093 
New York
1055 
Other values (37)
8819 

Length

Max length20
Median length7
Mean length7.501668057
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowU.S.
2nd rowOpinion
3rd rowOpinion
4th rowOpinion
5th rowOpinion

Common Values

ValueCountFrequency (%)
U.S.2364
14.1%
Opinion2272
13.5%
World1183
 
7.0%
Arts1093
 
6.5%
New York1055
 
6.3%
Business Day932
 
5.6%
The Learning Network708
 
4.2%
Real Estate687
 
4.1%
Well630
 
3.8%
Food573
 
3.4%
Other values (32)5289
31.5%

Length

2022-01-29T13:38:16.008231image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u.s2364
 
10.5%
opinion2272
 
10.1%
world1183
 
5.2%
arts1093
 
4.8%
new1055
 
4.7%
york1055
 
4.7%
the1052
 
4.7%
business932
 
4.1%
day932
 
4.1%
learning708
 
3.1%
Other values (45)9912
43.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

material
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
News
13088 
Op-Ed
2053 
Review
 
537
Interactive Feature
 
451
briefing
 
240
Other values (5)
 
417

Length

Max length19
Median length4
Mean length4.852972715
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInteractive Feature
2nd rowOp-Ed
3rd rowOp-Ed
4th rowOp-Ed
5th rowOp-Ed

Common Values

ValueCountFrequency (%)
News13088
78.0%
Op-Ed2053
 
12.2%
Review537
 
3.2%
Interactive Feature451
 
2.7%
briefing240
 
1.4%
Obituary (Obit)194
 
1.2%
Editorial159
 
0.9%
News Analysis59
 
0.4%
Letter3
 
< 0.1%
List2
 
< 0.1%

Length

2022-01-29T13:38:16.159242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-29T13:38:16.295252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
news13147
75.2%
op-ed2053
 
11.7%
review537
 
3.1%
interactive451
 
2.6%
feature451
 
2.6%
briefing240
 
1.4%
obituary194
 
1.1%
obit194
 
1.1%
editorial159
 
0.9%
analysis59
 
0.3%
Other values (2)5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

abstract
Categorical

HIGH CARDINALITY
UNIFORM

Distinct16543
Distinct (%)98.6%
Missing3
Missing (%)< 0.1%
Memory size131.3 KiB
The latest on stock market and business news during the coronavirus outbreak.
 
43
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.
 
31
Teenage comments in response to our recent writing prompts, and an invitation to join the ongoing conversation.
 
28
Recent residential sales in New York City and the region.
 
24
A look at one of the entries from last week’s puzzles that stumped our solvers.
 
20
Other values (16538)
16637 

Length

Max length626
Median length131
Mean length127.3022702
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16511 ?
Unique (%)98.4%

Sample

1st rowThe Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.
2nd rowIt has to do with respect.
3rd rowTara Reade’s allegations against Joe Biden demand action.
4th rowThe goal would be to renew faith in our government, but its effect would be the opposite.
5th rowThe competition among his cabineteers is fierce.

Common Values

ValueCountFrequency (%)
The latest on stock market and business news during the coronavirus outbreak.43
 
0.3%
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.31
 
0.2%
Teenage comments in response to our recent writing prompts, and an invitation to join the ongoing conversation.28
 
0.2%
Recent residential sales in New York City and the region.24
 
0.1%
A look at one of the entries from last week’s puzzles that stumped our solvers.20
 
0.1%
What is this image saying?14
 
0.1%
Live updates on stock market and business news during the coronavirus outbreak.14
 
0.1%
What story does this image inspire for you?13
 
0.1%
Our columnists and contributors give their rankings.13
 
0.1%
Appointment viewing is back. Find out what online events to look for today, and when to tune in.11
 
0.1%
Other values (16533)16572
98.7%

Length

2022-01-29T13:38:16.500268image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the20996
 
6.0%
a10474
 
3.0%
to9685
 
2.8%
of9148
 
2.6%
and9128
 
2.6%
in7416
 
2.1%
for3571
 
1.0%
is3210
 
0.9%
are2784
 
0.8%
on2757
 
0.8%
Other values (29100)272839
77.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

keywords
Categorical

HIGH CARDINALITY

Distinct15412
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
[]
 
720
['Crossword Puzzles']
 
206
['Coronavirus (2019-nCoV)']
 
159
['New York City']
 
52
['Customs, Etiquette and Manners']
 
21
Other values (15407)
15628 

Length

Max length1385
Median length160
Mean length170.9187418
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15314 ?
Unique (%)91.2%

Sample

1st row['Trump, Donald J', 'Trump Tax Returns', 'Conflicts of Interest', 'Trump Organization', 'Presidential Election of 2020', 'Tax Credits, Deductions and Exemptions', 'Income Tax', 'United States Politics and Government', 'Lobbying and Lobbyists', 'Miss Universe Organization', 'Consultants', 'Real Estate (Commercial)', 'Real Estate and Housing (Residential)', 'Hotels and Travel Lodgings', 'Golf']
2nd row['United States Politics and Government', 'Presidential Election of 2020', 'Presidents and Presidency (US)', 'Immigration and Emigration', 'Health Insurance and Managed Care', 'Illegal Immigration', 'Hydraulic Fracturing', 'Polls and Public Opinion', 'Voting and Voters', 'Democratic Party', 'Sanders, Bernard', 'Trump, Donald J', 'Liberalism (US Politics)', 'Conservatism (US Politics)']
3rd row['Biden, Joseph R Jr', 'Reade, Tara', 'Sex Crimes', '#MeToo Movement', 'Presidential Election of 2020', "Women's Rights", 'Democratic National Committee', 'Democratic Party', 'Democratic National Convention']
4th row['United States Politics and Government', 'Presidential Election of 2020', 'Constitution (US)', 'Law and Legislation', 'Justice Department', 'Trump, Donald J', 'Trump Tax Returns', 'Trump-Ukraine Whistle-blower Complaint and Impeachment Inquiry', 'Russian Interference in 2016 US Elections and Ties to Trump Associates', 'Presidents and Presidency (US)']
5th row['United States Politics and Government', 'Azar, Alex M II', 'Barr, William P', 'Chao, Elaine L', 'DeVos, Elizabeth (1958- )', 'Lighthizer, Robert E', 'Pence, Mike', 'Pompeo, Mike', 'Ross, Wilbur L Jr', 'Trump, Donald J', 'Wheeler, Andrew R', 'Wolf, Chad F', 'DeJoy, Louis', 'Bernhardt, David L']

Common Values

ValueCountFrequency (%)
[]720
 
4.3%
['Crossword Puzzles']206
 
1.2%
['Coronavirus (2019-nCoV)']159
 
0.9%
['New York City']52
 
0.3%
['Customs, Etiquette and Manners']21
 
0.1%
['Coronavirus (2019-nCoV)', 'New York State', 'New York City']19
 
0.1%
['New York City', 'Coronavirus (2019-nCoV)', 'Cuomo, Andrew M', 'de Blasio, Bill', 'New York State', 'Deaths (Fatalities)', 'Hospitals']14
 
0.1%
['George Floyd Protests (2020)']12
 
0.1%
['Presidential Election of 2020']10
 
0.1%
['Civilian Casualties', 'Taliban', 'AFGHANISTAN', 'Afghan National Security Forces', 'Kabul (Afghanistan)']8
 
< 0.1%
Other values (15402)15565
92.7%

Length

2022-01-29T13:38:16.690282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and26482
 
8.0%
coronavirus6579
 
2.0%
states5520
 
1.7%
2019-ncov5380
 
1.6%
united5342
 
1.6%
of4678
 
1.4%
politics3661
 
1.1%
government3628
 
1.1%
j3141
 
0.9%
3137
 
0.9%
Other values (18919)264393
79.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

word_count
Real number (ℝ≥0)

ZEROS

Distinct3035
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1300.961813
Minimum0
Maximum15619
Zeros451
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:16.902299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile230
Q1874
median1183
Q31502
95-th percentile2658.75
Maximum15619
Range15619
Interquartile range (IQR)628

Descriptive statistics

Standard deviation944.8018613
Coefficient of variation (CV)0.7262333541
Kurtosis23.89830344
Mean1300.961813
Median Absolute Deviation (MAD)312.5
Skewness3.733577943
Sum21837945
Variance892650.5571
MonotonicityNot monotonic
2022-01-29T13:38:17.093314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0451
 
2.7%
125532
 
0.2%
135226
 
0.2%
115026
 
0.2%
119026
 
0.2%
89326
 
0.2%
91725
 
0.1%
127925
 
0.1%
122423
 
0.1%
127723
 
0.1%
Other values (3025)16103
95.9%
ValueCountFrequency (%)
0451
2.7%
41
 
< 0.1%
101
 
< 0.1%
164
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
431
 
< 0.1%
461
 
< 0.1%
551
 
< 0.1%
ValueCountFrequency (%)
156191
< 0.1%
129471
< 0.1%
115381
< 0.1%
114091
< 0.1%
110841
< 0.1%
110201
< 0.1%
107081
< 0.1%
104961
< 0.1%
103291
< 0.1%
97811
< 0.1%
Distinct15399
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
Minimum2020-01-01 00:18:54+00:00
Maximum2020-12-31 18:02:02+00:00
2022-01-29T13:38:17.295327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-29T13:38:17.480343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

n_comments
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1905
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.0567139
Minimum1
Maximum8987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:17.829372image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q121
median87
Q3323
95-th percentile1358
Maximum8987
Range8986
Interquartile range (IQR)302

Descriptive statistics

Standard deviation513.4191755
Coefficient of variation (CV)1.728354053
Kurtosis18.51319882
Mean297.0567139
Median Absolute Deviation (MAD)79
Skewness3.470217812
Sum4986394
Variance263599.2498
MonotonicityDecreasing
2022-01-29T13:38:18.026382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2295
 
1.8%
1292
 
1.7%
3281
 
1.7%
5281
 
1.7%
6252
 
1.5%
4247
 
1.5%
7246
 
1.5%
8230
 
1.4%
10212
 
1.3%
12205
 
1.2%
Other values (1895)14245
84.9%
ValueCountFrequency (%)
1292
1.7%
2295
1.8%
3281
1.7%
4247
1.5%
5281
1.7%
6252
1.5%
7246
1.5%
8230
1.4%
9205
1.2%
10212
1.3%
ValueCountFrequency (%)
89871
< 0.1%
57021
< 0.1%
52281
< 0.1%
47601
< 0.1%
47181
< 0.1%
46871
< 0.1%
46101
< 0.1%
45951
< 0.1%
45321
< 0.1%
44861
< 0.1%

uniqueID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct16786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75
 
1
nyt://article/f48d3fc2-53ea-5fa5-a182-bf641a501563
 
1
nyt://article/682f745a-d103-5647-9c1f-bf96567b01be
 
1
nyt://article/e547a0c1-6f1f-5674-9a55-42e0c727968f
 
1
nyt://article/4642a70d-45d8-5102-bfaf-f141bc451480
 
1
Other values (16781)
16781 

Length

Max length54
Median length50
Mean length50.10747051
Min length50

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16786 ?
Unique (%)100.0%

Sample

1st rownyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75
2nd rownyt://article/57736deb-c3d4-5640-8732-bce6442e16c4
3rd rownyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf
4th rownyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbd
5th rownyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7

Common Values

ValueCountFrequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d751
 
< 0.1%
nyt://article/f48d3fc2-53ea-5fa5-a182-bf641a5015631
 
< 0.1%
nyt://article/682f745a-d103-5647-9c1f-bf96567b01be1
 
< 0.1%
nyt://article/e547a0c1-6f1f-5674-9a55-42e0c727968f1
 
< 0.1%
nyt://article/4642a70d-45d8-5102-bfaf-f141bc4514801
 
< 0.1%
nyt://article/ef5dfc4e-de9b-5935-a108-1ae44dc32ac71
 
< 0.1%
nyt://article/9d101233-1569-5433-9b0d-4da761da2efb1
 
< 0.1%
nyt://article/38693f15-3b09-5857-9792-ce468c394f4e1
 
< 0.1%
nyt://article/965642d3-0029-5faf-bfcc-4fc1fa0265571
 
< 0.1%
nyt://article/8ab11dea-c795-5a1d-ab04-2c8206c326fc1
 
< 0.1%
Other values (16776)16776
99.9%

Length

2022-01-29T13:38:18.200397image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d751
 
< 0.1%
nyt://article/4a3415ef-4390-577c-9cde-438b8b10bfd71
 
< 0.1%
nyt://article/a3d78b17-7bbc-52d1-9d0d-0250fb632dab1
 
< 0.1%
nyt://article/b781b70e-f02b-5bc1-b789-0194db88714b1
 
< 0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf1
 
< 0.1%
nyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbd1
 
< 0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c71
 
< 0.1%
nyt://article/120696c7-aa80-5be0-b7da-0649b449c9131
 
< 0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd11
 
< 0.1%
nyt://article/4974c21a-f80b-5d19-96e4-fed6eb0a7bbd1
 
< 0.1%
Other values (16776)16776
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lead_paragraph
Categorical

HIGH CARDINALITY

Distinct14994
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
[Want to get New York Today by email? Here’s the sign-up.]
 
229
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Spotify | Via Stitcher
 
209
 
191
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.
 
179
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.
 
88
Other values (14989)
15890 

Length

Max length1805
Median length228
Mean length241.3827594
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14813 ?
Unique (%)88.2%

Sample

1st rowThe Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.
2nd rowThe last four presidents — Bill Clinton, George W. Bush, Barack Obama and Donald Trump — are four very different politicians. But they have one crucial similarity: They all tried to appeal to voters who weren’t obvious supporters.
3rd rowIf you’re lucky when you report your sexual assault, you’ll become known as a person who was sexually assaulted. If you’re unlucky, you’ll become known as a person who lied about being sexually assaulted.
4th rowAs the Biden administration slowly coalesces, there have been many calls for its Justice Department to prosecute Donald Trump for any crimes he may have committed while in office. The hope, proponents of this view argue, is to establish that the president is subject to the rule of law and to deter future presidents from breaking the law.
5th rowOK, people, I know you’re feeling a little wan and helpless these days. Sure does seem like a long time until November.

Common Values

ValueCountFrequency (%)
[Want to get New York Today by email? Here’s the sign-up.]229
 
1.4%
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Spotify | Via Stitcher209
 
1.2%
191
 
1.1%
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.179
 
1.1%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.88
 
0.5%
Click on the slide show to see this week’s featured properties:70
 
0.4%
Dear Diary:52
 
0.3%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. If you’re interested in hearing from The Times regularly about great TV, sign up for our Watching newsletter and get recommendations straight to your inbox.48
 
0.3%
To hear more audio stories from publishers like The New York Times, download Audm for iPhone or Android.39
 
0.2%
This briefing is no longer updating. Read the latest developments in the coronavirus outbreak here.31
 
0.2%
Other values (14984)15650
93.2%

Length

2022-01-29T13:38:18.378409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the38183
 
5.6%
a19789
 
2.9%
of19263
 
2.8%
and17098
 
2.5%
to16898
 
2.5%
in14777
 
2.2%
on6856
 
1.0%
that6581
 
1.0%
for6477
 
1.0%
6344
 
0.9%
Other values (44608)527964
77.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.320743477
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:19.042482image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.43705575
Coefficient of variation (CV)0.5437739662
Kurtosis-1.210922789
Mean6.320743477
Median Absolute Deviation (MAD)3
Skewness0.09747293216
Sum106100
Variance11.81335223
MonotonicityNot monotonic
2022-01-29T13:38:19.177495image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
41666
9.9%
31644
9.8%
51538
9.2%
11443
8.6%
101415
8.4%
121338
8.0%
71317
7.8%
21312
7.8%
91307
7.8%
61306
7.8%
Other values (2)2500
14.9%
ValueCountFrequency (%)
11443
8.6%
21312
7.8%
31644
9.8%
41666
9.9%
51538
9.2%
61306
7.8%
71317
7.8%
81259
7.5%
91307
7.8%
101415
8.4%
ValueCountFrequency (%)
121338
8.0%
111241
7.4%
101415
8.4%
91307
7.8%
81259
7.5%
71317
7.8%
61306
7.8%
51538
9.2%
41666
9.9%
31644
9.8%

TEXT_Keywords_POS_NOUN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.038067437
Minimum0
Maximum23
Zeros2329
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:19.329504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.411232217
Coefficient of variation (CV)0.7936730395
Kurtosis2.313830009
Mean3.038067437
Median Absolute Deviation (MAD)2
Skewness1.111502034
Sum50997
Variance5.814040805
MonotonicityNot monotonic
2022-01-29T13:38:19.554523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
22933
17.5%
32772
16.5%
12668
15.9%
02329
13.9%
42080
12.4%
51534
9.1%
61020
 
6.1%
7638
 
3.8%
8360
 
2.1%
9192
 
1.1%
Other values (13)260
 
1.5%
ValueCountFrequency (%)
02329
13.9%
12668
15.9%
22933
17.5%
32772
16.5%
42080
12.4%
51534
9.1%
61020
 
6.1%
7638
 
3.8%
8360
 
2.1%
9192
 
1.1%
ValueCountFrequency (%)
231
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
182
 
< 0.1%
177
 
< 0.1%
161
 
< 0.1%
155
 
< 0.1%
148
 
< 0.1%
1320
0.1%

TEXT_Keywords_POS_PNOUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.80900751
Minimum0
Maximum146
Zeros779
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:19.866544image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median12
Q319
95-th percentile31
Maximum146
Range146
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.945965656
Coefficient of variation (CV)0.7202520276
Kurtosis10.37938259
Mean13.80900751
Median Absolute Deviation (MAD)6
Skewness1.859670049
Sum231798
Variance98.92223283
MonotonicityNot monotonic
2022-01-29T13:38:20.155568image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10839
 
5.0%
9817
 
4.9%
8805
 
4.8%
11789
 
4.7%
6788
 
4.7%
0779
 
4.6%
12766
 
4.6%
7733
 
4.4%
14707
 
4.2%
2691
 
4.1%
Other values (79)9072
54.0%
ValueCountFrequency (%)
0779
4.6%
191
 
0.5%
2691
4.1%
3412
2.5%
4520
3.1%
5640
3.8%
6788
4.7%
7733
4.4%
8805
4.8%
9817
4.9%
ValueCountFrequency (%)
1461
< 0.1%
1321
< 0.1%
1191
< 0.1%
1132
< 0.1%
1121
< 0.1%
1111
< 0.1%
1101
< 0.1%
1061
< 0.1%
1051
< 0.1%
1021
< 0.1%

TEXT_Keywords_ENT_ORG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.514416776
Minimum0
Maximum18
Zeros5460
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:20.371587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.617063671
Coefficient of variation (CV)1.067779819
Kurtosis4.262192957
Mean1.514416776
Median Absolute Deviation (MAD)1
Skewness1.577332615
Sum25421
Variance2.614894915
MonotonicityNot monotonic
2022-01-29T13:38:20.529595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
05460
32.5%
14513
26.9%
23114
18.6%
31880
 
11.2%
4911
 
5.4%
5465
 
2.8%
6240
 
1.4%
7106
 
0.6%
846
 
0.3%
929
 
0.2%
Other values (8)22
 
0.1%
ValueCountFrequency (%)
05460
32.5%
14513
26.9%
23114
18.6%
31880
 
11.2%
4911
 
5.4%
5465
 
2.8%
6240
 
1.4%
7106
 
0.6%
846
 
0.3%
929
 
0.2%
ValueCountFrequency (%)
181
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
124
 
< 0.1%
116
 
< 0.1%
107
 
< 0.1%
929
0.2%
846
0.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
0
15585 
1
 
1123
2
 
72
3
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
015585
92.8%
11123
 
6.7%
272
 
0.4%
36
 
< 0.1%

Length

2022-01-29T13:38:20.736610image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-29T13:38:20.855623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
015585
92.8%
11123
 
6.7%
272
 
0.4%
36
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.3 KiB
0
16557 
1
 
199
2
 
27
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016557
98.6%
1199
 
1.2%
227
 
0.2%
32
 
< 0.1%
41
 
< 0.1%

Length

2022-01-29T13:38:20.968629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-29T13:38:21.073638image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
016557
98.6%
1199
 
1.2%
227
 
0.2%
32
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_GPE
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8454664601
Minimum0
Maximum37
Zeros8526
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:21.167643image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.218004638
Coefficient of variation (CV)1.440630345
Kurtosis63.77050509
Mean0.8454664601
Median Absolute Deviation (MAD)0
Skewness4.131694158
Sum14192
Variance1.483535299
MonotonicityNot monotonic
2022-01-29T13:38:21.281653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
08526
50.8%
14757
28.3%
22147
 
12.8%
3815
 
4.9%
4323
 
1.9%
5104
 
0.6%
652
 
0.3%
729
 
0.2%
812
 
0.1%
96
 
< 0.1%
Other values (8)15
 
0.1%
ValueCountFrequency (%)
08526
50.8%
14757
28.3%
22147
 
12.8%
3815
 
4.9%
4323
 
1.9%
5104
 
0.6%
652
 
0.3%
729
 
0.2%
812
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
371
 
< 0.1%
241
 
< 0.1%
152
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
115
< 0.1%
102
 
< 0.1%
96
< 0.1%
812
0.1%

TEXT_Keywords_ENT_LOC
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03872274514
Minimum0
Maximum5
Zeros16219
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:21.405664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2224823015
Coefficient of variation (CV)5.745519866
Kurtosis69.56136531
Mean0.03872274514
Median Absolute Deviation (MAD)0
Skewness7.177187438
Sum650
Variance0.04949837448
MonotonicityNot monotonic
2022-01-29T13:38:21.532671image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
016219
96.6%
1499
 
3.0%
257
 
0.3%
38
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
016219
96.6%
1499
 
3.0%
257
 
0.3%
38
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
42
 
< 0.1%
38
 
< 0.1%
257
 
0.3%
1499
 
3.0%
016219
96.6%

TEXT_Keywords_ENT_PERSON
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.864768259
Minimum0
Maximum40
Zeros5541
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size131.3 KiB
2022-01-29T13:38:21.676683image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.277985511
Coefficient of variation (CV)1.221591745
Kurtosis13.0983628
Mean1.864768259
Median Absolute Deviation (MAD)1
Skewness2.461893008
Sum31302
Variance5.189217989
MonotonicityNot monotonic
2022-01-29T13:38:21.835697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
05541
33.0%
14009
23.9%
22612
15.6%
31597
 
9.5%
41197
 
7.1%
5669
 
4.0%
6457
 
2.7%
7249
 
1.5%
8150
 
0.9%
991
 
0.5%
Other values (16)214
 
1.3%
ValueCountFrequency (%)
05541
33.0%
14009
23.9%
22612
15.6%
31597
 
9.5%
41197
 
7.1%
5669
 
4.0%
6457
 
2.7%
7249
 
1.5%
8150
 
0.9%
991
 
0.5%
ValueCountFrequency (%)
401
 
< 0.1%
301
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
187
< 0.1%
175
< 0.1%
167
< 0.1%

comment_isHigh
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
0
12838 
1
3948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
012838
76.5%
13948
 
23.5%

Length

2022-01-29T13:38:22.000706image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-29T13:38:22.094716image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
012838
76.5%
13948
 
23.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

newsdesk_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.14738473
Minimum0
Maximum62
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-01-29T13:38:22.216724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q114
median33
Q341
95-th percentile60
Maximum62
Range62
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.11737567
Coefficient of variation (CV)0.5677897379
Kurtosis-1.014975335
Mean30.14738473
Median Absolute Deviation (MAD)13
Skewness0.1496767391
Sum506054
Variance293.0045499
MonotonicityNot monotonic
2022-01-29T13:38:22.391737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341996
 
11.9%
141073
 
6.4%
91049
 
6.2%
6981
 
5.8%
59843
 
5.0%
25820
 
4.9%
41750
 
4.5%
30715
 
4.3%
21691
 
4.1%
40682
 
4.1%
Other values (53)7186
42.8%
ValueCountFrequency (%)
08
 
< 0.1%
1266
 
1.6%
22
 
< 0.1%
36
 
< 0.1%
4195
 
1.2%
554
 
0.3%
6981
5.8%
79
 
0.1%
8145
 
0.9%
91049
6.2%
ValueCountFrequency (%)
6220
 
0.1%
61615
3.7%
60213
 
1.3%
59843
5.0%
588
 
< 0.1%
57280
 
1.7%
5611
 
0.1%
55105
 
0.6%
54205
 
1.2%
5352
 
0.3%

section_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.02764208
Minimum0
Maximum41
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-01-29T13:38:22.585751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median20
Q335
95-th percentile40
Maximum41
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.01067238
Coefficient of variation (CV)0.5452545641
Kurtosis-1.154425339
Mean22.02764208
Median Absolute Deviation (MAD)10
Skewness-0.06340488543
Sum369756
Variance144.2562511
MonotonicityNot monotonic
2022-01-29T13:38:22.760764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
362364
14.1%
182272
13.5%
401183
 
7.0%
11093
 
6.5%
161055
 
6.3%
6932
 
5.6%
30708
 
4.2%
22687
 
4.1%
39630
 
3.8%
11573
 
3.4%
Other values (32)5289
31.5%
ValueCountFrequency (%)
01
 
< 0.1%
11093
6.5%
25
 
< 0.1%
31
 
< 0.1%
4371
 
2.2%
53
 
< 0.1%
6932
5.6%
7148
 
0.9%
8468
2.8%
97
 
< 0.1%
ValueCountFrequency (%)
414
 
< 0.1%
401183
7.0%
39630
 
3.8%
383
 
< 0.1%
375
 
< 0.1%
362364
14.1%
35213
 
1.3%
34115
 
0.7%
33245
 
1.5%
326
 
< 0.1%

material_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.474025974
Minimum0
Maximum9
Zeros159
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-01-29T13:38:22.918777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q34
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.479664063
Coefficient of variation (CV)0.3307231722
Kurtosis1.954423472
Mean4.474025974
Median Absolute Deviation (MAD)0
Skewness0.8273478618
Sum75101
Variance2.189405739
MonotonicityNot monotonic
2022-01-29T13:38:23.030786image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
413088
78.0%
72053
 
12.2%
8537
 
3.2%
1451
 
2.7%
9240
 
1.4%
6194
 
1.2%
0159
 
0.9%
559
 
0.4%
23
 
< 0.1%
32
 
< 0.1%
ValueCountFrequency (%)
0159
 
0.9%
1451
 
2.7%
23
 
< 0.1%
32
 
< 0.1%
413088
78.0%
559
 
0.4%
6194
 
1.2%
72053
 
12.2%
8537
 
3.2%
9240
 
1.4%
ValueCountFrequency (%)
9240
 
1.4%
8537
 
3.2%
72053
 
12.2%
6194
 
1.2%
559
 
0.4%
413088
78.0%
32
 
< 0.1%
23
 
< 0.1%
1451
 
2.7%
0159
 
0.9%

Interactions

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Correlations

2022-01-29T13:38:23.155797image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-29T13:38:23.434040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-29T13:38:23.698063image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-29T13:38:23.987082image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-29T13:38:24.217099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-29T13:38:14.139630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-29T13:38:15.029703image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-29T13:38:15.409873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

newsdesksectionmaterialabstractkeywordsword_countpub_daten_commentsuniqueIDlead_paragraphmonthTEXT_Keywords_POS_NOUNTEXT_Keywords_POS_PNOUNTEXT_Keywords_ENT_ORGTEXT_Keywords_ENT_NORPTEXT_Keywords_ENT_FACTEXT_Keywords_ENT_GPETEXT_Keywords_ENT_LOCTEXT_Keywords_ENT_PERSONcomment_isHighnewsdesk_numsection_nummaterial_num
0U.S.U.S.Interactive FeatureThe Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.['Trump, Donald J', 'Trump Tax Returns', 'Conflicts of Interest', 'Trump Organization', 'Presidential Election of 2020', 'Tax Credits, Deductions and Exemptions', 'Income Tax', 'United States Politics and Government', 'Lobbying and Lobbyists', 'Miss Universe Organization', 'Consultants', 'Real Estate (Commercial)', 'Real Estate and Housing (Residential)', 'Hotels and Travel Lodgings', 'Golf']02020-09-27 21:07:33+00:008987nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75The Times obtained Donald Trump’s tax information extending over more than two decades, revealing struggling properties, vast write-offs, an audit battle and hundreds of millions in debt coming due.9928500003155361
1OpEdOpinionOp-EdIt has to do with respect.['United States Politics and Government', 'Presidential Election of 2020', 'Presidents and Presidency (US)', 'Immigration and Emigration', 'Health Insurance and Managed Care', 'Illegal Immigration', 'Hydraulic Fracturing', 'Polls and Public Opinion', 'Voting and Voters', 'Democratic Party', 'Sanders, Bernard', 'Trump, Donald J', 'Liberalism (US Politics)', 'Conservatism (US Politics)']9342020-02-23 23:33:29+00:005702nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4The last four presidents — Bill Clinton, George W. Bush, Barack Obama and Donald Trump — are four very different politicians. But they have one crucial similarity: They all tried to appeal to voters who weren’t obvious supporters.2730200103134187
2OpEdOpinionOp-EdTara Reade’s allegations against Joe Biden demand action.['Biden, Joseph R Jr', 'Reade, Tara', 'Sex Crimes', '#MeToo Movement', 'Presidential Election of 2020', "Women's Rights", 'Democratic National Committee', 'Democratic Party', 'Democratic National Convention']9862020-05-03 19:00:07+00:005228nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aafIf you’re lucky when you report your sexual assault, you’ll become known as a person who was sexually assaulted. If you’re unlucky, you’ll become known as a person who lied about being sexually assaulted.5415300002134187
3OpEdOpinionOp-EdThe goal would be to renew faith in our government, but its effect would be the opposite.['United States Politics and Government', 'Presidential Election of 2020', 'Constitution (US)', 'Law and Legislation', 'Justice Department', 'Trump, Donald J', 'Trump Tax Returns', 'Trump-Ukraine Whistle-blower Complaint and Impeachment Inquiry', 'Russian Interference in 2016 US Elections and Ties to Trump Associates', 'Presidents and Presidency (US)']9842020-12-03 10:00:12+00:004760nyt://article/7e29ac1e-fd93-5e9f-ab32-55ef8947ccbdAs the Biden administration slowly coalesces, there have been many calls for its Justice Department to prosecute Donald Trump for any crimes he may have committed while in office. The hope, proponents of this view argue, is to establish that the president is subject to the rule of law and to deter future presidents from breaking the law.12430300102134187
4OpEdOpinionOp-EdThe competition among his cabineteers is fierce.['United States Politics and Government', 'Azar, Alex M II', 'Barr, William P', 'Chao, Elaine L', 'DeVos, Elizabeth (1958- )', 'Lighthizer, Robert E', 'Pence, Mike', 'Pompeo, Mike', 'Ross, Wilbur L Jr', 'Trump, Donald J', 'Wheeler, Andrew R', 'Wolf, Chad F', 'DeJoy, Louis', 'Bernhardt, David L']8862020-08-05 23:40:48+00:004718nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7OK, people, I know you’re feeling a little wan and helpless these days. Sure does seem like a long time until November.80442101016134187
5MetroNew YorkNewsVideo of the incident touched off intense discussions about the history of black people being falsely reported to the police.['Black People', 'Blacks', 'Central Park (Manhattan, NY)', 'Race and Ethnicity', 'Social Media', 'Cooper, Amy (May 25, 2020 Central Park Incident)', 'Discrimination', 'Dogs', 'Birdwatching', 'Cooper, Christian (Birder)', 'Video Recordings, Downloads and Streaming']12112020-05-26 12:13:27+00:004687nyt://article/120696c7-aa80-5be0-b7da-0649b449c913[The latest: Amy Cooper, the white woman in Central Park who called the police on a Black bird-watcher, will be charged with filing a false report.]5719210110125164
6OpEdOpinionOp-EdHe is not a liberal, he’s the end of liberalism.['Sanders, Bernard', 'Democratic Party', 'Presidential Election of 2020', 'Liberalism (US Politics)', 'United States Politics and Government', 'Trump, Donald J']8882020-02-28 00:00:07+00:004610nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd1A few months ago, I wrote a column saying I would vote for Elizabeth Warren over Donald Trump. I may not agree with some of her policies, but culture is more important than politics. She does not spread moral rot the way Trump does.2114200003134187
7WashingtonWorldNewsSuleimani was planning attacks on Americans across the region, leading to an airstrike in Baghdad, the Pentagon statement said. Iran’s supreme leader called for vengeance.['Iran', 'Suleimani, Qassim', 'Defense and Military Forces', 'Quds Force', 'Islamic Revolutionary Guards Corps', 'United States International Relations', 'United States Defense and Military Forces', 'Targeted Killings', 'Drones (Pilotless Planes)', 'Trump, Donald J', 'Baghdad International Airport (Iraq)', 'Iraq', 'Popular Mobilization Forces (Iraq)']23332020-01-03 01:02:54+00:004595nyt://article/4974c21a-f80b-5d19-96e4-fed6eb0a7bbdWEST PALM BEACH, Fla. — Iran’s top security and intelligence commander was killed early Friday in a drone strike at Baghdad International Airport that was authorized by President Trump, American officials said.1335301203159404
8WashingtonU.S.NewsAn examination reveals the president was warned about the potential for a pandemic but that internal divisions, lack of planning and his faith in his own instincts led to a halting response.['Coronavirus (2019-nCoV)', 'Trump, Donald J', 'United States Politics and Government', 'United States Economy', 'United States International Relations', 'Presidential Election of 2020', 'Shutdowns (Institutional)', 'Quarantines', 'China', 'Travel Warnings', 'Centers for Disease Control and Prevention', 'National Institutes of Health', 'National Security Council', 'Health and Human Services Department', 'Azar, Alex M II', 'Birx, Deborah L', 'Fauci, Anthony S', 'Mnuchin, Steven T', 'Navarro, Peter', "O'Brien, Robert C (1952- )", 'Pence, Mike', 'Pottinger, Matthew', 'Redfield, Robert R']53562020-04-11 16:13:09+00:004532nyt://article/0c53a478-7276-5fe7-abb7-e9272ed0f06b46588001013159364
9NationalU.S.NewsPolice officers used flash grenades to disperse a crowd so the president could visit for a photo opportunity. And in New York, protesters and looters defied a curfew.['Police Brutality, Misconduct and Shootings', 'Demonstrations, Protests and Riots', 'United States', 'Minneapolis (Minn)', 'George Floyd Protests (2020)']37872020-06-01 09:13:44+00:004486nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430[Follow our live coverage of the George Floyd protests.]6113010201130364

Last rows

newsdesksectionmaterialabstractkeywordsword_countpub_daten_commentsuniqueIDlead_paragraphmonthTEXT_Keywords_POS_NOUNTEXT_Keywords_POS_PNOUNTEXT_Keywords_ENT_ORGTEXT_Keywords_ENT_NORPTEXT_Keywords_ENT_FACTEXT_Keywords_ENT_GPETEXT_Keywords_ENT_LOCTEXT_Keywords_ENT_PERSONcomment_isHighnewsdesk_numsection_nummaterial_num
16776LearningThe Learning NetworkNewsIn this lesson, students will take a virtual field trip through some of the world’s greatest museums.[]6052020-04-30 13:31:57+00:001nyt://article/14d2dd3b-d670-5801-af70-8ff2db7f4b2fFeatured Article: “Now Virtual and in Video, Museum Websites Shake Off the Dust” by Jason Farago400000000021304
16777CultureArtsNewsAppointment viewing is back. Find out what online events to look for today, and when to tune in.['Cinco de Mayo', 'Music', 'Television', 'Theater', 'Quarantine (Life and Culture)', 'Dancing', 'Coronavirus (2019-nCoV)', 'Video Recordings, Downloads and Streaming', 'Adlon, Pamela', 'Cantone, Mario', 'Longoria, Eva', 'Grammy Museum', 'New York City Ballet', 'Ninety-Second Street Y', 'Valenzuela, Francisca (1987- )', 'Santos, Enrique (Music Executive)']4272020-05-05 09:00:33+00:001nyt://article/5e3a69b5-5ccb-597f-822f-f9d6e2c889afHere are a few of the best events happening on Tuesday and how to tune in (all times are Eastern).57313001040914
16778CultureArtsNewsYotam Haber’s “Estro Poetico-Armonico III” combines live singing with archival recordings of cantors.['Jews and Judaism', 'Classical Music', 'Azrieli Foundation', 'Awards, Decorations and Honors', 'Haber, Yotam', 'Estro Poetico-Armonico III (Musical Work)']10112020-10-20 14:47:43+00:001nyt://article/35b54def-309e-52a3-8c82-6a5133e0f6caSince early in his career, Yotam Haber has grappled with what it means to be a contemporary Jewish composer. The tentative answers offered by his music — full of allusions, distortion and whispers of the past — suggest that the grappling itself is a vital part of that identity.103142001020914
16779PodcastsPodcastsNewsOur interview with a longtime protester about her journey to the front lines.['The Daily (Radio Program)', 'Podcasts', 'Newsletters', 'New York Times', 'Elections', 'Presidential Election of 2020', 'Race and Ethnicity', 'Police Brutality, Misconduct and Shootings', 'Black Lives Matter Movement', 'Demonstrations, Protests and Riots', 'George Floyd Protests (2020)']9262020-08-07 22:10:23+00:001nyt://article/b00bc7b4-64c1-575b-8753-6097f0c3f300Producer Sydney Harper on Wednesday’s episode:8519110002038204
16780StylesStyleNewsRachel Comey and Vaquera get New York Fashion Week off to a buzzy start.['Fashion and Apparel', 'New York Fashion Week', 'Fashion Shows', 'Rag & Bone (Fashion Label)', 'Burch, Tory', 'Maxwell, Brandon (1984- )', 'Rogers, Christopher John', 'Comey, Rachel', 'your-feed-fashion']11672020-02-10 16:18:52+00:001nyt://article/003acab0-8d5d-5dd7-905e-4ac90f9d706eNew York Fashion Week dribbled into existence just after President Trump’s impeachment acquittal victory lap, and in the shadow of the Bernie-Buttigieg squabbles over Iowa. Not to mention the Oscars.2320100004046264
16781SportsSportsNewsComputer-vs.-computer games of FIFA livestream to gamblers on Twitch. Fantasy contests carry League of Legends lineups. In the coronavirus age, video games have grown into a darling for casinos.['E-Sports', 'Casinos', 'Computer and Video Games', 'Coronavirus (2019-nCoV)', 'Regulation and Deregulation of Industry', 'Nevada Gaming Control Board', 'Gambling', 'Quarantine (Life and Culture)']19552020-06-08 07:00:22+00:001nyt://article/159dda5b-2815-51ba-ad15-00bcc9c73dfdMarco Blume, trading director for the sports book Pinnacle, remembers when betting on the competitive video games known as e-sports was an exotic concept.6414200001044254
16782SpecialSectionsArtsNewsPublic monuments, and the artists who create them, are beginning to represent women and their achievements.['Monuments and Memorials (Structures)', 'Women and Girls', "Women's Rights", 'Sculpture', 'Art', 'Bergmann, Meredith (Artist)', 'Central Park (Manhattan, NY)', 'Kentucky', 'Matthews, Amanda (1968- )']10772020-10-21 09:00:32+00:001nyt://article/ab567633-47cb-56ff-8cf0-29b49e2873f3This article is part of our latest Fine Arts & Exhibits special report, which focuses on how art endures and inspires, even in the darkest of times.1071330021104314
16783SpecialSectionsArtsNewsThe visitors may be masked, but the art is gradually coming into full view.['Museums', 'Coronavirus Reopenings', 'AMERICAN MUSEUM OF NATURAL HISTORY', 'Dallas Museum of Art', 'Gardner, Isabella Stewart, Museum', 'Los Angeles County Museum of Art', 'Metropolitan Museum of Art', 'Museum of Fine Arts (Boston)', 'Museum of Modern Art', 'National Gallery of Art', 'Whitney Museum of American Art', 'Perez, Jorge M, Art Museum of Miami-Dade County', 'Museum of Fine Arts (Houston)', 'Cleveland Museum of Art', 'Sirmans, Franklin', 'Tinterow, Gary', 'Weinberg, Adam D', 'Zumthor, Peter']16422020-10-21 09:00:29+00:001nyt://article/a7c443c8-1e8f-5426-b6d6-54347c448eceThis article is part of our latest Fine Arts & Exhibits special report, which focuses on how art endures and inspires, even in the darkest of times.1025791020604314
16784NewsDeskArtsNewsPlus, the road to women’s suffrage, finding a new normal and more live Times events this week.[]10002020-05-18 09:00:10+00:001nyt://article/ef863416-0249-5def-a51b-ae7bd6ed3b32The coronavirus has transformed nearly every aspect of daily life, but Times journalists are here to keep you informed and connected from home. Below is a selection of our live events this week (all times are Eastern). You can find the full calendar here.50000000003214
16785ObitsObituariesNewsA member of the Black Panthers, he helped lead a historic, and successful, sit-in in San Francisco as part of a nationwide anti-discrimination campaign on behalf of people with disabilities.['Demonstrations, Protests and Riots', 'Disabilities', 'Black People', 'Blacks', 'Discrimination', 'Black Panther Party', 'Califano, Joseph A Jr', 'Heumann, Judith (1947- )', 'California', 'AMERICANS WITH DISABILITIES ACT', 'Lomax, Brad (1950-84)']13762020-07-08 20:48:22+00:001nyt://article/5b1bf30b-7f4c-5c15-9910-55217ddd2b82Overlooked is a series of obituaries about remarkable people whose deaths, beginning in 1851, went unreported in The Times. This latest installment is part of a series exploring how the Americans With Disabilities Act has shaped modern life for disabled people. Share your stories or email us at ada@nytimes.com.7518110104033174